An empirical validation of software cost estimation models
Communications of the ACM
Estimating Software Project Effort Using Analogies
IEEE Transactions on Software Engineering
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
COCOMO evaluation and tailoring
ICSE '85 Proceedings of the 8th international conference on Software engineering
A replicated assessment and comparison of common software cost modeling techniques
Proceedings of the 22nd international conference on Software engineering
Swarm intelligence
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
Enhancing the Cocomo Estimation Models
IEEE Software
An introduction to variable and feature selection
The Journal of Machine Learning Research
Feature subset selection can improve software cost estimation accuracy
PROMISE '05 Proceedings of the 2005 workshop on Predictor models in software engineering
Finding the Right Data for Software Cost Modeling
IEEE Software
A Systematic Review of Software Development Cost Estimation Studies
IEEE Transactions on Software Engineering
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Software development projects are notorious for being completed behind schedule and over budget and for often failing to meet user requirements. A variety of cost estimation models have been proposed to predict development costs early in the lifecycle with the hope of managing the project well within time and budget. However, studies have reported rather high error rates of prediction even in the case of the well-established and widely acknowledged models. This study focuses on the improvement and fine-tuning of the COCOMO 81 model through the application of the recently developed Swarm Intelligence techniques. Recent studies have used data mining techniques to improve the prediction accuracy of COCOMO 81. Our research reconfirms these studies and makes further improvement in the prediction accuracy. The wrapper data mining method is slow and is heavily dependent on the domain experts' heuristics. The Particle Swarm Optimization POS meta-heuristic algorithm is fast converging and relies neither on the knowledge of the problem nor on the experts' heuristics, making its application wide and extensive.